To give you the best online STOXX experience, we and selected partners use cookies on our site. If you continue using our website, we will assume you are happy to receive all cookies on the STOXX website. To find out more information about cookies and how they're used on the STOXX website, visit our privacy policy.

The adoption of AI has been driven by both quantitative investment managers trying to uncover new sources of alpha, but also by more traditional managers facing pressure on profit margins and poor returns in a low-interest rate environment. The use of AI has increased speed, improved accuracy and reduced costs, particularly in trading and execution. Smart algorithms, for example, can execute large volumes of trades at improved costs and managed market impact.

But it has been the astonishing surge in data volumes, particularly concerning alternative data, which has propelled asset managers to invest in AI tools.

In a survey produced by the McKinsey Global Institute,1 financial services appears as the third industry among 13 by AI adoption, with around 28% of companies in the sector embracing one or more AI technologies at scale or in a core part of their business. Financial services is the top industry when ranked by planned average change in AI investments in the next three years.

A breakdown of AI and the machine learning evolution As firms adapt and evolve through the use of AI, the concept itself has undergone its own metamorphosis. Dr. Anand Rao, Global AI Lead and Innovation Lead at PwC’s Data and Analytics practice, says in a recent report2 that the progress is best understood as a continuum of three phases:

• Assisted intelligence: Already used in our everyday lives, it is the replacement of repetitive and standardized tasks such as data processing and voice recognition. • Augmented intelligence or machine learning: It is where humans and computers work together. An example would be ‘deep learning,’ where algorithms incorporate the ability to learn through trial and error and become autonomous in identifying patterns and take decisions. • Autonomous intelligence: It is the stage where the decision lies almost solely with the machine. Currently relatively an underdeveloped segment, but applications could comprise continuous learning capabilities.

The asset-management industry would be well into the second phase. But while there are varying levels of augmented intelligence techniques in use, it is in the realm of investing decisions where we could see a transformational leap.

Data is integral to the investment process Until recently, investment decisions relied upon structured data such as earnings calls and research reports, with computers programmed to search through this information to identify trends. Now, with so much data from a wide variety of sources (web filings, credit-card purchases and even satellite imagery), intelligent programs are designed to provide deeper statistical analysis and learn autonomously by trial and error.

There is no question that original, accurate raw data is integral to the investment process. But to capture, clean, process and automate this data is an immense challenge. A survey by Barclays3 has found that despite the widespread use of alternative data, 80% of surveyed investment managers said that their biggest challenge was in assessing its usefulness. They need advanced data analysis techniques that can provide them with an informational edge.

Artificial neural networks The power of autonomous and cognitive identification of data trends by machines presents with a solution to this challenge.

Powered by a system of neural networks (similar to the workings of a brain), the field of computer science that allows algorithms to predict outcomes, known as machine learning, could generate new and as yet undiscovered ideas for investing.

According to Barclays,3 62% of hedge-fund managers following so-called systematic strategies use machine-learning techniques within the investment process.

Letting machines read the data When it comes to qualitative information, artificial intelligence is also playing a role in helping money managers scroll through large repositories of data and spot valuable information.

Here, the fast-developing technology of natural language processing (NLP) is gaining ground. More than just about translation and voice activation, recent technological improvements mean that it is now possible for NLP to cut through the mass of information in earnings calls and company reports, comprehending nuances such as semantics, tone and voice; and subsequently make predictions of stock market performance.

New artificial intelligence systems are also improving the capability of web scraping, where machines can look through an endless amount of web pages in search of specific information, and decide on their own when alternative observation is needed.

Machine learning tools such as statistical models that predict outcomes will be particularly prevalent within the realm of passive investing, which according to Moody’s Investors Service will achieve a leading share of the US market by 2024 or sooner.4

The case for investing in AI It can already be inferred that all this uniquely intelligent technology is very valuable on its own, and that it helps generate economic gains for its adopters. In an upcoming article we’ll look at the case for investing in the providers of AI software, algorithms and products, and in their consumers, which stand to reap economic benefits.

We might be witnessing the most defining period in the evolution of asset management. AI has triggered unprecedented changes at every level, and the continuing accelerated pace of innovation promises uncharted but exciting times for the industry.

You want to receive our PULSE ONLINE mailing that updates you on new articles? Please send us an email to pulse@stoxx.com. You can share your feedback, comments or questions by sending an email to the same address.